import streamlit as st import random import pandas as pd import requests from io import BytesIO from PIL import Image from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM import re import time # --------------------------- Configuration & Session State --------------------------- # Define maximum dimensions for the fortune image (in pixels) MAX_SIZE = (400, 400) # Initialize button click count in session state if "button_count_temp" not in st.session_state: st.session_state.button_count_temp = 0 # Set page configuration and title st.set_page_config(page_title="Fortune Stick Enquiry", layout="wide") st.title("Fortune Stick Enquiry") # Initialize session state variables for managing application state if "submitted_text" not in st.session_state: st.session_state.submitted_text = False if "fortune_number" not in st.session_state: st.session_state.fortune_number = None if "fortune_row" not in st.session_state: st.session_state.fortune_row = None if "error_message" not in st.session_state: st.session_state.error_message = "" if "cfu_explain_text" not in st.session_state: st.session_state.cfu_explain_text = "" if "stick_clicked" not in st.session_state: st.session_state.stick_clicked = False # Load fortune details from CSV file into session state if "fortune_data" not in st.session_state: try: st.session_state.fortune_data = pd.read_csv("/home/user/app/resources/detail.csv") except Exception as e: st.error(f"Error loading CSV: {e}") st.session_state.fortune_data = None # --------------------------- Model Functions --------------------------- # Function to load a fine-tuned classifier model and predict a label based on the question def load_finetuned_classifier_model(question): label_list = ["Geomancy", "Lost Property", "Personal Well-Being", "Future Prospect", "Traveling"] # Mapping to convert default "LABEL_x" outputs to meaningful labels mapping = {f"LABEL_{i}": label for i, label in enumerate(label_list)} pipe = pipeline("text-classification", model="tonyhui2234/CustomModel_classifier_model_10") prediction = pipe(question)[0]['label'] predicted_label = mapping.get(prediction, prediction) return predicted_label # Function to generate a detailed answer by combining the user's question and the fortune detail def generate_answer(question, fortune): # Start measuring runtime start_time = time.perf_counter() tokenizer = AutoTokenizer.from_pretrained("tonyhui2234/finetuned_model_text_gen") model = AutoModelForSeq2SeqLM.from_pretrained("tonyhui2234/finetuned_model_text_gen", device_map="auto") input_text = "Question: " + question + " Fortune: " + fortune inputs = tokenizer(input_text, return_tensors="pt", truncation=True) outputs = model.generate( **inputs, max_length=256, num_beams=4, early_stopping=True, repetition_penalty=2.0, no_repeat_ngram_size=3 ) answer = tokenizer.decode(outputs[0], skip_special_tokens=True) # Stop measuring runtime run_time = time.perf_counter() - start_time print(f"Runtime: {run_time:.4f} seconds") return answer # Function that combines analysis with regex to extract the related fortune detail and then generate an answer def analysis(row_detail, classifiy, question): # Use the classifier's output to match the corresponding detail in the fortune data pattern = re.compile(re.escape(classifiy) + r":\s*(.*?)(?:\.|$)", re.IGNORECASE) match = pattern.search(row_detail) if match: result = match.group(1) # Generate a custom answer based on the matched fortune detail and the user's question return generate_answer(question, result) else: return "Heaven's secret cannot be revealed." # Function to check if the input sentence is in English using a language detection model def check_sentence_is_english_model(question): pipe_english = pipeline("text-classification", model="eleldar/language-detection") return pipe_english(question)[0]['label'] == 'en' # Function to check if the input sentence is a question using a question vs. statement classifier def check_sentence_is_question_model(question): pipe_question = pipeline("text-classification", model="shahrukhx01/question-vs-statement-classifier") return pipe_question(question)[0]['label'] == 'LABEL_1' # --------------------------- Callback Functions --------------------------- # Callback for when the submit button is clicked def submit_text_callback(): question = st.session_state.get("user_sentence", "") # Clear any previous error message st.session_state.error_message = "" # Validate that the input is in English and is a question if not check_sentence_is_english_model(question): st.session_state.error_message = "Please enter in English!" st.session_state.button_count_temp = 0 return if not check_sentence_is_question_model(question): st.session_state.error_message = "This is not a question. Please enter again!" st.session_state.button_count_temp = 0 return # Require a second confirmation click to proceed if st.session_state.button_count_temp == 0: st.session_state.error_message = "Please take a moment to quietly reflect on your question in your mind, then click submit again!" st.session_state.button_count_temp = 1 return # If validations pass, set submission flag and reset click counter st.session_state.submitted_text = True st.session_state.button_count_temp = 0 # Randomly generate a fortune number between 1 and 100 st.session_state.fortune_number = random.randint(1, 100) # Retrieve corresponding fortune details from the CSV based on the generated number df = st.session_state.fortune_data row_detail = '' if df is not None: matching_row = df[df['CNumber'] == st.session_state.fortune_number] if not matching_row.empty: row = matching_row.iloc[0] row_detail = row.get("Detail", "No detail available.") st.session_state.fortune_row = { "Header": row.get("Header", "N/A"), "Luck": row.get("Luck", "N/A"), "Description": row.get("Description", "No description available."), "Detail": row_detail, "HeaderLink": row.get("link", None) } else: st.session_state.fortune_row = { "Header": "N/A", "Luck": "N/A", "Description": "No description available.", "Detail": "No detail available.", "HeaderLink": None } # Function to load and resize a local image file def load_and_resize_image(path, max_size=MAX_SIZE): try: img = Image.open(path) img.thumbnail(max_size, Image.Resampling.LANCZOS) return img except Exception as e: st.error(f"Error loading image: {e}") return None # Function to download an image from a URL and resize it def download_and_resize_image(url, max_size=MAX_SIZE): try: response = requests.get(url) response.raise_for_status() image_bytes = BytesIO(response.content) img = Image.open(image_bytes) img.thumbnail(max_size, Image.Resampling.LANCZOS) return img except Exception as e: st.error(f"Error loading image from URL: {e}") return None # Callback for when the 'Cfu Explain' button is clicked def stick_enquiry_callback(): # Retrieve the user's question and ensure fortune data is available question = st.session_state.get("user_sentence", "") if not st.session_state.fortune_row: st.error("Fortune data is not available. Please submit your question first.") return row_detail = st.session_state.fortune_row.get("Detail", "No detail available.") # Classify the question to determine which fortune detail to use classifiy = load_finetuned_classifier_model(question) # Generate an explanation based on the classification and fortune detail cfu_explain = analysis(row_detail, classifiy, question) # Save the generated explanation for display st.session_state.cfu_explain_text = cfu_explain st.session_state.stick_clicked = True # --------------------------- Layout & Display --------------------------- # Define the main layout with two columns: left for user input and right for fortune display left_col, _, right_col = st.columns([3, 1, 5]) # ---- Left Column: User Input and Interaction ---- with left_col: left_top = st.container() left_bottom = st.container() # Top container: Question input and submission button with left_top: st.text_area("Enter your question in English", key="user_sentence", height=150) st.button("submit", key="submit_button", on_click=submit_text_callback) if st.session_state.error_message: st.error(st.session_state.error_message) # Bottom container: Button to trigger explanation and display the generated answer if st.session_state.submitted_text: with left_bottom: # Add spacing for better visual separation for _ in range(5): st.write("") col1, col2, col3 = st.columns(3) with col2: st.button("Cfu Explain", key="stick_button", on_click=stick_enquiry_callback) if st.session_state.stick_clicked: # Display the generated explanation text st.text_area(' ', value=st.session_state.cfu_explain_text, height=300, disabled=True) # ---- Right Column: Fortune Display (Image and Details) ---- with right_col: with st.container(): col_left, col_center, col_right = st.columns([1, 2, 1]) with col_center: # Display fortune image based on fortune data availability if st.session_state.submitted_text and st.session_state.fortune_row: header_link = st.session_state.fortune_row.get("HeaderLink") if header_link: img_from_url = download_and_resize_image(header_link) if img_from_url: st.image(img_from_url, use_container_width=False) else: img = load_and_resize_image("/home/user/app/resources/error.png") if img: st.image(img, use_container_width=False) else: img = load_and_resize_image("/home/user/app/resources/error.png") if img: st.image(img, use_container_width=False) else: img = load_and_resize_image("/home/user/app/resources/fortune.png") if img: st.image(img, caption="Your Fortune", use_container_width=False) with st.container(): # Display fortune details: Number, Luck, Description, and Detail if st.session_state.fortune_row: luck_text = st.session_state.fortune_row.get("Luck", "N/A") description_text = st.session_state.fortune_row.get("Description", "No description available.") detail_text = st.session_state.fortune_row.get("Detail", "No detail available.") summary = f"""
Fortune stick number: {st.session_state.fortune_number}
Luck: {luck_text}
""" st.markdown(summary, unsafe_allow_html=True) st.text_area("Description", value=description_text, height=150, disabled=True) st.text_area("Detail", value=detail_text, height=150, disabled=True)